Chapter 6 Diversity analysis
6.1 Alpha diversity
# Calculate Hill numbers
richness <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 0) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(richness = 1) %>%
rownames_to_column(var = "sample")
neutral <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(neutral = 1) %>%
rownames_to_column(var = "sample")
phylogenetic <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1, tree = genome_tree) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(phylogenetic = 1) %>%
rownames_to_column(var = "sample")
# Aggregate basal GIFT into elements
dist <- genome_gifts %>%
to.elements(., GIFT_db) %>%
traits2dist(., method = "gower")
functional <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1, dist = dist) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(functional = 1) %>%
rownames_to_column(var = "sample") %>%
mutate(functional = if_else(is.nan(functional), 1, functional))
# Merge all metrics
alpha_div <- richness %>%
full_join(neutral, by = join_by(sample == sample)) %>%
full_join(phylogenetic, by = join_by(sample == sample)) %>%
full_join(functional, by = join_by(sample == sample))6.1.1 Wild samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="0_Wild") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.58) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")6.1.2 Acclimation samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.58) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")6.1.3 Antibiotics samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="2_Antibiotics") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.58) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")6.1.4 Transplant_1 samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="3_Transplant1") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")6.1.5 Transplant_2 samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="4_Transplant2") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")6.1.6 Post-Transplant_1 samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")6.1.7 Post-Transplant_2 samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-control","Warm-control", "Cold-intervention"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-control","Warm-control", "Cold-intervention"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")6.2 Beta diversity
beta_q0n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 0)
beta_q1n <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 1)
beta_q1p <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 1, tree = genome_tree)
beta_q1f <- genome_counts_filt %>%
column_to_rownames(., "genome") %>%
hillpair(., q = 1, dist = dist)6.3 Permanovas
6.3.1 1. Are the wild populations similar?
6.3.1.1 Wild: P.muralis vs P.liolepis
wild <- meta %>%
filter(time_point == "0_Wild")
# Create a temporary modified version of genome_counts_filt
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
wild.counts <- temp_genome_counts[, which(colnames(temp_genome_counts) %in% rownames(wild))]
identical(sort(colnames(wild.counts)), sort(as.character(rownames(wild))))
wild_nmds <- sample_metadata %>%
filter(time_point == "0_Wild")6.3.1.3 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.000012 0.000012 0.0012 999 0.969
Residuals 25 0.257281 0.010291
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Podarcis_liolepis Podarcis_muralis
Podarcis_liolepis 0.966
Podarcis_muralis 0.97302
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| species | 1 | 1.542719 | 0.2095041 | 6.625717 | 0.001 |
| Residual | 25 | 5.820951 | 0.7904959 | NA | NA |
| Total | 26 | 7.363669 | 1.0000000 | NA | NA |
6.3.1.4 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.000048 0.0000476 0.0044 999 0.955
Residuals 25 0.270114 0.0108046
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Podarcis_liolepis Podarcis_muralis
Podarcis_liolepis 0.957
Podarcis_muralis 0.94763
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| species | 1 | 1.918266 | 0.2608511 | 8.822682 | 0.001 |
| Residual | 25 | 5.435610 | 0.7391489 | NA | NA |
| Total | 26 | 7.353876 | 1.0000000 | NA | NA |
6.3.1.5 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.03585 0.035847 2.4912 999 0.122
Residuals 25 0.35973 0.014389
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Podarcis_liolepis Podarcis_muralis
Podarcis_liolepis 0.119
Podarcis_muralis 0.12705
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| species | 1 | 0.3218613 | 0.2162815 | 6.899207 | 0.001 |
| Residual | 25 | 1.1662981 | 0.7837185 | NA | NA |
| Total | 26 | 1.4881594 | 1.0000000 | NA | NA |
6.3.1.6 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.018367 0.018367 1.5597 999 0.222
Residuals 25 0.294402 0.011776
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Podarcis_liolepis Podarcis_muralis
Podarcis_liolepis 0.226
Podarcis_muralis 0.22328
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| species | 1 | 0.0858578 | 0.172879 | 5.225323 | 0.053 |
| Residual | 25 | 0.4107775 | 0.827121 | NA | NA |
| Total | 26 | 0.4966352 | 1.000000 | NA | NA |
beta_q0n_nmds_wild <- beta_div_richness_wild$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(wild_nmds, by = join_by(sample == Tube_code))
beta_q1n_nmds_wild <- beta_div_neutral_wild$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(wild_nmds, by = join_by(sample == Tube_code))
beta_q1p_nmds_wild <- beta_div_phylo_wild$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(wild_nmds, by = join_by(sample == Tube_code))
beta_q1f_nmds_wild <- beta_div_func_wild$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(wild_nmds, by = join_by(sample == Tube_code))6.3.2 2. Effect of acclimation
accli <- meta %>%
filter(time_point == "1_Acclimation")
# Create a temporary modified version of genome_counts_filt
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
accli.counts <- temp_genome_counts[, which(colnames(temp_genome_counts) %in% rownames(accli))]
identical(sort(colnames(accli.counts)), sort(as.character(rownames(accli))))
accli_nmds <- sample_metadata %>%
filter(time_point == "1_Acclimation")6.3.2.2 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.11796 0.117959 12.963 999 0.001 ***
Residuals 25 0.22748 0.009099
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.002
Hot_dry 0.0013711
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Population | 1 | 1.639807 | 0.179834 | 5.481634 | 0.001 |
| Residual | 25 | 7.478640 | 0.820166 | NA | NA |
| Total | 26 | 9.118447 | 1.000000 | NA | NA |
6.3.2.3 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.07844 0.078443 5.2384 999 0.032 *
Residuals 25 0.37437 0.014975
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.026
Hot_dry 0.030815
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Population | 1 | 1.947003 | 0.2306127 | 7.493387 | 0.001 |
| Residual | 25 | 6.495736 | 0.7693873 | NA | NA |
| Total | 26 | 8.442739 | 1.0000000 | NA | NA |
6.3.2.4 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.06739 0.067395 2.9532 999 0.099 .
Residuals 25 0.57052 0.022821
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.084
Hot_dry 0.098068
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Population | 1 | 0.2441653 | 0.1224638 | 3.488854 | 0.023 |
| Residual | 25 | 1.7496100 | 0.8775362 | NA | NA |
| Total | 26 | 1.9937754 | 1.0000000 | NA | NA |
6.3.2.5 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.02496 0.024955 0.6729 999 0.437
Residuals 25 0.92714 0.037085
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.424
Hot_dry 0.41979
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Population | 1 | 0.0279454 | 0.0248037 | 0.6358634 | 0.425 |
| Residual | 25 | 1.0987171 | 0.9751963 | NA | NA |
| Total | 26 | 1.1266624 | 1.0000000 | NA | NA |
beta_q0n_nmds_accli <- beta_div_richness_accli$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli_nmds, by = join_by(sample == Tube_code))
beta_q1n_nmds_accli <- beta_div_neutral_accli$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli_nmds, by = join_by(sample == Tube_code))
beta_q1p_nmds_accli <- beta_div_phylo_accli$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli_nmds, by = join_by(sample == Tube_code))
beta_q1f_nmds_accli <- beta_div_func_accli$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli_nmds, by = join_by(sample == Tube_code))6.3.3 3. Comparison between Wild and Acclimation
accli1 <- meta %>%
filter(time_point == "0_Wild" | time_point == "1_Acclimation")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
accli1.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(accli1))]
identical(sort(colnames(accli1.counts)),sort(as.character(rownames(accli1))))
accli1_nmds <- sample_metadata %>%
filter(time_point == "0_Wild" | time_point == "1_Acclimation")6.3.3.1 Number of samples used
[1] 54
beta_div_richness_accli1<-hillpair(data=accli1.counts, q=0)
beta_div_neutral_accli1<-hillpair(data=accli1.counts, q=1)
beta_div_phylo_accli1<-hillpair(data=accli1.counts, q=1, tree=genome_tree)
beta_div_func_accli1<-hillpair(data=accli1.counts, q=1, dist=dist)6.3.3.1.1 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.05014 0.050145 6.2252 999 0.013 *
Residuals 52 0.41886 0.008055
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
0_Wild 1_Acclimation
0_Wild 0.018
1_Acclimation 0.015808
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 0.6172653 | 0.0360987 | 2.320618 | 0.003 |
| species | 1 | 2.8279677 | 0.1653842 | 10.631785 | 0.004 |
| time_point:species | 1 | 0.3545578 | 0.0207351 | 1.332965 | 0.054 |
| Residual | 50 | 13.2995905 | 0.7777820 | NA | NA |
| Total | 53 | 17.0993812 | 1.0000000 | NA | NA |
6.3.3.1.2 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.0199 0.0199035 2.1213 999 0.149
Residuals 52 0.4879 0.0093827
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
0_Wild 1_Acclimation
0_Wild 0.149
1_Acclimation 0.15128
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 0.9050519 | 0.0541893 | 3.792749 | 0.001 |
| species | 1 | 3.3236300 | 0.1989999 | 13.928143 | 0.001 |
| time_point:species | 1 | 0.5416391 | 0.0324302 | 2.269815 | 0.004 |
| Residual | 50 | 11.9313461 | 0.7143805 | NA | NA |
| Total | 53 | 16.7016671 | 1.0000000 | NA | NA |
6.3.3.1.3 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01334 0.013340 0.6524 999 0.42
Residuals 52 1.06332 0.020449
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
0_Wild 1_Acclimation
0_Wild 0.425
1_Acclimation 0.42294
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 0.2890434 | 0.0766494 | 4.956318 | 0.002 |
| species | 1 | 0.3508889 | 0.0930498 | 6.016803 | 0.001 |
| time_point:species | 1 | 0.2151377 | 0.0570509 | 3.689034 | 0.007 |
| Residual | 50 | 2.9159082 | 0.7732498 | NA | NA |
| Total | 53 | 3.7709782 | 1.0000000 | NA | NA |
6.3.3.1.4 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.0123 0.012300 0.4817 999 0.494
Residuals 52 1.3277 0.025533
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
0_Wild 1_Acclimation
0_Wild 0.493
1_Acclimation 0.49073
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 0.0448774 | 0.0269021 | 1.4865056 | 0.253 |
| species | 1 | 0.0973005 | 0.0583275 | 3.2229509 | 0.334 |
| time_point:species | 1 | 0.0165026 | 0.0098926 | 0.5466273 | 0.396 |
| Residual | 50 | 1.5094945 | 0.9048777 | NA | NA |
| Total | 53 | 1.6681751 | 1.0000000 | NA | NA |
beta_richness_nmds_accli1 <- beta_div_richness_accli1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli1_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_accli1 <- beta_div_neutral_accli1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli1_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_accli1 <- beta_div_phylo_accli1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli1_nmds, by = join_by(sample == Tube_code))
beta_func_nmds_accli1 <- beta_div_func_accli1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(accli1_nmds, by = join_by(sample == Tube_code))6.3.4 4. Do the antibiotics work?
6.3.4.1 Antibiotics
treat1 <- meta %>%
filter(time_point == "2_Antibiotics")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
treat1.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(treat1))]
identical(sort(colnames(treat1.counts)),sort(as.character(rownames(treat1))))
treat1_nmds <- sample_metadata %>%
filter(time_point == "2_Antibiotics")6.3.4.2 Number of samples used
[1] 23
beta_div_richness_treat1<-hillpair(data=treat1.counts, q=0)
beta_div_neutral_treat1<-hillpair(data=treat1.counts, q=1)
beta_div_phylo_treat1<-hillpair(data=treat1.counts, q=1, tree=genome_tree)
beta_div_func_treat1<-hillpair(data=treat1.counts, q=1, dist=dist)6.3.4.2.1 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.015319 0.0153186 6.8764 999 0.022 *
Residuals 21 0.046782 0.0022277
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.017
Hot_dry 0.015919
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Population | 1 | 1.356644 | 0.1527052 | 3.784762 | 0.001 |
| Residual | 21 | 7.527429 | 0.8472948 | NA | NA |
| Total | 22 | 8.884073 | 1.0000000 | NA | NA |
6.3.4.2.2 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.030536 0.0305358 3.8593 999 0.064 .
Residuals 21 0.166158 0.0079123
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.063
Hot_dry 0.062842
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Population | 1 | 1.785669 | 0.2085055 | 5.532084 | 0.001 |
| Residual | 21 | 6.778468 | 0.7914945 | NA | NA |
| Total | 22 | 8.564137 | 1.0000000 | NA | NA |
6.3.4.2.3 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.012041 0.012041 0.9898 999 0.336
Residuals 21 0.255459 0.012165
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.326
Hot_dry 0.33111
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Population | 1 | 0.8963254 | 0.1888758 | 4.889993 | 0.002 |
| Residual | 21 | 3.8492558 | 0.8111242 | NA | NA |
| Total | 22 | 4.7455811 | 1.0000000 | NA | NA |
6.3.4.2.4 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01802 0.018021 0.4386 999 0.503
Residuals 21 0.86280 0.041086
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.512
Hot_dry 0.51499
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Population | 1 | 0.0184663 | 0.0098404 | 0.2087022 | 0.719 |
| Residual | 21 | 1.8581156 | 0.9901596 | NA | NA |
| Total | 22 | 1.8765819 | 1.0000000 | NA | NA |
beta_richness_nmds_treat1 <- beta_div_richness_treat1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat1_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_treat1 <- beta_div_neutral_treat1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat1_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_treat1 <- beta_div_phylo_treat1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat1_nmds, by = join_by(sample == Tube_code))
beta_func_nmds_treat1 <- beta_div_func_treat1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat1_nmds, by = join_by(sample == Tube_code))6.3.4.3 Acclimation vs antibiotics
treat <- meta %>%
filter(time_point == "1_Acclimation" | time_point == "2_Antibiotics")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
treat.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(treat))]
identical(sort(colnames(treat.counts)),sort(as.character(rownames(treat))))
treat_nmds <- sample_metadata %>%
filter(time_point == "1_Acclimation" | time_point == "2_Antibiotics")6.3.4.4 Number of samples used
[1] 50
beta_div_richness_treat<-hillpair(data=treat.counts, q=0)
beta_div_neutral_treat<-hillpair(data=treat.counts, q=1)
beta_div_phylo_treat<-hillpair(data=treat.counts, q=1, tree=genome_tree)
beta_div_func_treat<-hillpair(data=treat.counts, q=1, dist=dist)6.3.4.4.1 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.025318 0.0253178 6.021 999 0.015 *
Residuals 48 0.201837 0.0042049
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 2_Antibiotics
1_Acclimation 0.014
2_Antibiotics 0.017817
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 1.8885838 | 0.0949462 | 5.789315 | 0.001 |
| Population | 1 | 2.1171094 | 0.1064350 | 6.489843 | 0.001 |
| time_point:Population | 1 | 0.8793415 | 0.0442078 | 2.695557 | 0.002 |
| Residual | 46 | 15.0060684 | 0.7544111 | NA | NA |
| Total | 49 | 19.8911031 | 1.0000000 | NA | NA |
6.3.4.4.2 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.039587 0.039587 6.8387 999 0.013 *
Residuals 48 0.277854 0.005789
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 2_Antibiotics
1_Acclimation 0.016
2_Antibiotics 0.011886
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 2.0241808 | 0.1063620 | 7.014531 | 0.001 |
| Population | 1 | 2.8531033 | 0.1499183 | 9.887052 | 0.001 |
| time_point:Population | 1 | 0.8795688 | 0.0462175 | 3.048030 | 0.002 |
| Residual | 46 | 13.2742044 | 0.6975022 | NA | NA |
| Total | 49 | 19.0310573 | 1.0000000 | NA | NA |
6.3.4.4.3 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.58372 0.58372 35.413 999 0.001 ***
Residuals 48 0.79119 0.01648
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 2_Antibiotics
1_Acclimation 0.001
2_Antibiotics 2.9795e-07
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 1.8065206 | 0.2113909 | 14.842282 | 0.001 |
| Population | 1 | 0.7903334 | 0.0924813 | 6.493340 | 0.001 |
| time_point:Population | 1 | 0.3501572 | 0.0409738 | 2.876874 | 0.036 |
| Residual | 46 | 5.5988658 | 0.6551540 | NA | NA |
| Total | 49 | 8.5458771 | 1.0000000 | NA | NA |
6.3.4.4.4 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.18591 0.185914 5.0679 999 0.018 *
Residuals 48 1.76088 0.036685
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 2_Antibiotics
1_Acclimation 0.023
2_Antibiotics 0.028989
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 1.8020952 | 0.3750193 | 28.0355332 | 0.001 |
| Population | 1 | 0.0031247 | 0.0006503 | 0.0486115 | 0.001 |
| time_point:Population | 1 | 0.0432870 | 0.0090081 | 0.6734238 | 0.460 |
| Residual | 46 | 2.9568327 | 0.6153223 | NA | NA |
| Total | 49 | 4.8053396 | 1.0000000 | NA | NA |
beta_richness_nmds_treat <- beta_div_richness_treat$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_treat <- beta_div_neutral_treat$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_treat <- beta_div_phylo_treat$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat_nmds, by = join_by(sample == Tube_code))
beta_func_nmds_treat <- beta_div_func_treat$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat_nmds, by = join_by(sample == Tube_code))6.3.5 5. Does the FMT work?
6.3.5.1 Comparison between FMT2 vs Post-FMT2
#Create newID to identify duplicated samples
transplants_metadata<-sample_metadata%>%
mutate(Tube_code=str_remove_all(Tube_code, "_a"))
transplants_metadata$newID <- paste(transplants_metadata$Tube_code, "_", transplants_metadata$individual)
transplant3<-transplants_metadata%>%
filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2")%>%
column_to_rownames("newID")
transplant3_nmds <- transplants_metadata %>%
filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2")
full_counts<-temp_genome_counts %>%
t()%>%
as.data.frame()%>%
rownames_to_column("Tube_code")%>%
full_join(transplants_metadata,by = join_by(Tube_code == Tube_code))
transplant3_counts<-full_counts %>%
filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2") %>%
subset(select=-c(315:324)) %>%
column_to_rownames("newID")%>%
subset(select=-c(1))%>%
t() %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric)
identical(sort(colnames(transplant3_counts)),sort(as.character(rownames(transplant3))))6.3.5.2 Number of samples used
[1] 49
beta_div_richness_transplant3<-hillpair(data=transplant3_counts, q=0)
beta_div_neutral_transplant3<-hillpair(data=transplant3_counts, q=1)
beta_div_phylo_transplant3<-hillpair(data=transplant3_counts, q=1, tree=genome_tree)
beta_div_func_transplant3<-hillpair(data=transplant3_counts, q=1, dist=dist)#Arrange of metadata dataframe
transplant3_arrange<-transplant3[labels(beta_div_neutral_transplant3$S),]6.3.5.2.1 Richness
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| species | 1 | 1.180473 | 0.0855095 | 5.155229 | 0.022 |
| time_point | 1 | 0.860906 | 0.0623612 | 3.759652 | 0.001 |
| type | 1 | 1.459433 | 0.1057165 | 6.373471 | 0.002 |
| Residual | 45 | 10.304350 | 0.7464128 | NA | NA |
| Total | 48 | 13.805162 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 1.4169018 | 5.739828 | 0.15622903 | 0.001 | 0.003 | * |
| Control vs Hot_control | 1 | 2.0940966 | 8.509112 | 0.21005427 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.3004618 | 1.265034 | 0.04179854 | 0.140 | 0.420 |
6.3.5.2.2 Neutral
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| species | 1 | 1.2800927 | 0.0939787 | 6.068484 | 0.008 |
| time_point | 1 | 0.9350566 | 0.0686477 | 4.432785 | 0.001 |
| type | 1 | 1.9135997 | 0.1404879 | 9.071725 | 0.001 |
| Residual | 45 | 9.4923500 | 0.6968858 | NA | NA |
| Total | 48 | 13.6210990 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 1.8758788 | 8.282671 | 0.21084796 | 0.001 | 0.003 | * |
| Control vs Hot_control | 1 | 2.4396317 | 10.635546 | 0.24945256 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.3158428 | 1.394345 | 0.04587515 | 0.096 | 0.288 |
6.3.5.2.3 Phylogenetic
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| species | 1 | 0.1400466 | 0.0952654 | 5.873615 | 0.059 |
| time_point | 1 | 0.1138047 | 0.0774145 | 4.773017 | 0.001 |
| type | 1 | 0.1432667 | 0.0974558 | 6.008665 | 0.003 |
| Residual | 45 | 1.0729504 | 0.7298643 | NA | NA |
| Total | 48 | 1.4700683 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 0.14387705 | 5.735321 | 0.15612552 | 0.001 | 0.003 | * |
| Control vs Hot_control | 1 | 0.22715701 | 9.044894 | 0.22036587 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.04648319 | 1.704277 | 0.05550617 | 0.103 | 0.309 |
6.3.5.2.4 Functional
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| species | 1 | 0.0092808 | 0.0077189 | 0.3741811 | 0.390 |
| time_point | 1 | -0.0061674 | -0.0051295 | -0.2486581 | 0.859 |
| type | 1 | 0.0831052 | 0.0691191 | 3.3506286 | 0.240 |
| Residual | 45 | 1.1161295 | 0.9282915 | NA | NA |
| Total | 48 | 1.2023481 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 0.078539743 | 4.59293783 | 0.129040706 | 0.070 | 0.210 | |
| Control vs Hot_control | 1 | 0.052468954 | 2.13675422 | 0.062593948 | 0.218 | 0.654 | |
| Treatment vs Hot_control | 1 | -0.002340352 | -0.07432315 | -0.002569452 | 0.860 | 1.000 |
beta_richness_nmds_transplant3 <- beta_div_richness_transplant3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant3_nmds, by = join_by(sample == newID))
beta_neutral_nmds_transplant3 <- beta_div_neutral_transplant3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant3_nmds, by = join_by(sample == newID))
beta_phylo_nmds_transplant3 <- beta_div_phylo_transplant3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant3_nmds, by = join_by(sample == newID))
beta_func_nmds_transplant3 <- beta_div_func_transplant3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant3_nmds, by = join_by(sample == newID))p0<-beta_richness_nmds_transplant3 %>%
group_by(individual) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_transplant3 %>%
group_by(individual) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylo_nmds_transplant3 %>%
group_by(individual) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")
p3<-beta_func_nmds_transplant3 %>%
group_by(individual) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
theme_classic()+
theme(legend.position="none")6.3.5.3 Comparison between the different experimental time points (Acclimation vs Transplant samples)
The estimated time for calculating the 5151 pairwise combinations is 38 seconds.
6.3.5.4 Comparison of acclimation samples to transplant samples
transplant7<-transplants_metadata%>%
filter(time_point == "4_Transplant2" | time_point == "1_Acclimation"| time_point == "3_Transplant1")%>%
column_to_rownames("newID")
transplant7_nmds <- transplants_metadata %>%
filter(time_point == "4_Transplant2" | time_point == "1_Acclimation"| time_point == "3_Transplant1")
transplant7_counts<-full_counts %>%
filter(time_point == "4_Transplant2" | time_point == "1_Acclimation"| time_point == "3_Transplant1") %>%
subset(select=-c(315:324)) %>%
column_to_rownames("newID")%>%
subset(select=-c(1))%>%
t() %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric)
transplant7_counts <- transplant7_counts[, !names(transplant7_counts) %in% c("AD45 _ LI1_2nd_2", "AD48 _ LI1_2nd_6")]
identical(sort(colnames(transplant7_counts)),sort(as.character(rownames(transplant7))))[1] TRUE
6.3.5.5 Number of samples used
[1] 73
beta_div_richness_transplant7<-hillpair(data=transplant7_counts, q=0)
beta_div_neutral_transplant7<-hillpair(data=transplant7_counts, q=1)
beta_div_phylo_transplant7<-hillpair(data=transplant7_counts, q=1, tree=genome_tree)
beta_div_func_transplant7<-hillpair(data=transplant7_counts, q=1, dist=dist)#Arrange of metadata dataframe
transplant7_arrange<-transplant7[labels(beta_div_neutral_transplant7$S),]
transplant7_arrange <- transplant7_arrange %>%
mutate(time_point = recode(time_point,
"3_Transplant1" = "Transplant",
"4_Transplant2" = "Transplant"))
transplant7_arrange$type_time <- interaction(transplant7_arrange$type, transplant7_arrange$time_point)6.3.5.5.1 Richness
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Population | 1 | 2.3108184 | 0.1096208 | 9.6707812 | 0.078 |
| time_point | 2 | 1.1082036 | 0.0525710 | 2.3189174 | 0.001 |
| type | 1 | 1.4676332 | 0.0696217 | 6.1420489 | 0.038 |
| Population:time_point | 2 | 0.4228641 | 0.0200599 | 0.8848438 | 0.463 |
| Residual | 66 | 15.7705995 | 0.7481267 | NA | NA |
| Total | 72 | 21.0801189 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.36208146 | 1.0521088 | 0.06169963 | 0.321 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.28008774 | 4.6054436 | 0.22350616 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.Transplant | 1 | 0.55038651 | 2.2107376 | 0.08124505 | 0.004 | 0.060 | |
| Control.1_Acclimation vs Treatment.Transplant | 1 | 1.62289430 | 6.7106689 | 0.25123553 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Hot_control.Transplant | 1 | 1.73215888 | 7.4315069 | 0.25250175 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.36066298 | 5.0871520 | 0.24124415 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.Transplant | 1 | 0.52860586 | 2.1820402 | 0.08027507 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Treatment.Transplant | 1 | 1.76810026 | 7.5736721 | 0.27467042 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.Transplant | 1 | 1.87790626 | 8.3291875 | 0.27462613 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.Transplant | 1 | 1.75314247 | 8.7706781 | 0.25971282 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.Transplant | 1 | 0.27700454 | 1.5346880 | 0.07126586 | 0.079 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.Transplant | 1 | 0.26448976 | 1.4916174 | 0.06349573 | 0.103 | 1.000 | |
| Control.Transplant vs Treatment.Transplant | 1 | 2.30884687 | 12.4299510 | 0.30002331 | 0.001 | 0.015 | . |
| Control.Transplant vs Hot_control.Transplant | 1 | 2.50396161 | 13.6713271 | 0.30604256 | 0.001 | 0.015 | . |
| Treatment.Transplant vs Hot_control.Transplant | 1 | 0.01688622 | 0.1023282 | 0.00392027 | 1.000 | 1.000 |
6.3.5.5.2 Neutral
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Population | 1 | 2.641095 | 0.1266245 | 12.453113 | 0.056 |
| time_point | 2 | 1.394944 | 0.0668791 | 3.288673 | 0.001 |
| type | 1 | 1.523725 | 0.0730534 | 7.184566 | 0.021 |
| time_point:type | 4 | 1.724614 | 0.0826848 | 2.032946 | 0.015 |
| Residual | 64 | 13.573319 | 0.6507583 | NA | NA |
| Total | 72 | 20.857698 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.23160196 | 0.7712905 | 0.045988741 | 0.719 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.40153474 | 5.7562378 | 0.264578733 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.Transplant | 1 | 0.56111203 | 2.5583085 | 0.092832565 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Treatment.Transplant | 1 | 1.88709838 | 8.3257794 | 0.293929402 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Hot_control.Transplant | 1 | 2.02585000 | 9.2317432 | 0.295588471 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.63477039 | 6.8326887 | 0.299250291 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.Transplant | 1 | 0.61335323 | 2.8313912 | 0.101733730 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.Transplant | 1 | 2.10939140 | 9.4473664 | 0.320822116 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.Transplant | 1 | 2.24827218 | 10.3907678 | 0.320794118 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.Transplant | 1 | 1.87351542 | 10.3925002 | 0.293635661 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.Transplant | 1 | 0.34276062 | 1.9273510 | 0.087897118 | 0.041 | 0.615 | |
| Hot_control.1_Acclimation vs Hot_control.Transplant | 1 | 0.31638309 | 1.8072337 | 0.075911118 | 0.054 | 0.810 | |
| Control.Transplant vs Treatment.Transplant | 1 | 2.48701901 | 14.0199769 | 0.325894571 | 0.001 | 0.015 | . |
| Control.Transplant vs Hot_control.Transplant | 1 | 2.75304261 | 15.6912860 | 0.336064549 | 0.001 | 0.015 | . |
| Treatment.Transplant vs Hot_control.Transplant | 1 | 0.01764676 | 0.1022118 | 0.003915827 | 0.993 | 1.000 |
6.3.5.5.3 Phylogenetic
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Population | 1 | 0.3001462 | 0.0764583 | 6.402286 | 0.320 |
| time_point | 2 | 0.2984483 | 0.0760258 | 3.183035 | 0.003 |
| type | 1 | 0.1391084 | 0.0354361 | 2.967261 | 0.625 |
| Residual | 68 | 3.1879143 | 0.8120798 | NA | NA |
| Total | 72 | 3.9256172 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.04186923 | 0.43916424 | 0.026714511 | 0.763 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.15609416 | 2.55468892 | 0.137684276 | 0.032 | 0.480 | |
| Control.1_Acclimation vs Control.Transplant | 1 | 0.03888650 | 0.83961027 | 0.032493148 | 0.448 | 1.000 | |
| Control.1_Acclimation vs Treatment.Transplant | 1 | 0.28946588 | 4.58406811 | 0.186464994 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Hot_control.Transplant | 1 | 0.31864880 | 5.37781508 | 0.196429666 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.05218385 | 0.202081922 | 0.004 | 0.060 | |
| Treatment.1_Acclimation vs Control.Transplant | 1 | 0.11794420 | 2.69844074 | 0.097422117 | 0.045 | 0.675 | |
| Treatment.1_Acclimation vs Treatment.Transplant | 1 | 0.37640156 | 6.28511923 | 0.239113210 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.Transplant | 1 | 0.40433696 | 7.18306079 | 0.246138020 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.Transplant | 1 | 0.11597038 | 5.32063275 | 0.175478948 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.Transplant | 1 | 0.03673004 | 1.13023077 | 0.053488804 | 0.364 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.Transplant | 1 | 0.04097680 | 1.30539166 | 0.056012432 | 0.253 | 1.000 | |
| Control.Transplant vs Treatment.Transplant | 1 | 0.21736741 | 7.59281199 | 0.207494630 | 0.001 | 0.015 | . |
| Control.Transplant vs Hot_control.Transplant | 1 | 0.25837791 | 9.19762187 | 0.228810100 | 0.001 | 0.015 | . |
| Treatment.Transplant vs Hot_control.Transplant | 1 | 0.00180330 | 0.04804393 | 0.001844435 | 0.968 | 1.000 |
6.3.5.5.4 Functional
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Population | 1 | 0.0968624 | 0.0459940 | 4.124655 | 0.431 |
| time_point | 2 | 0.1660358 | 0.0788403 | 3.535121 | 0.025 |
| type | 1 | 0.2461830 | 0.1168973 | 10.483121 | 0.160 |
| Residual | 68 | 1.5968952 | 0.7582683 | NA | NA |
| Total | 72 | 2.1059764 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.0904501448 | 1.65575459 | 0.0937798825 | 0.206 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.0857692246 | 1.63605364 | 0.0927675587 | 0.233 | 1.000 | |
| Control.1_Acclimation vs Control.Transplant | 1 | 0.0705970944 | 2.02878350 | 0.0750601115 | 0.173 | 1.000 | |
| Control.1_Acclimation vs Treatment.Transplant | 1 | 0.3221196060 | 7.21718333 | 0.2651701040 | 0.006 | 0.090 | |
| Control.1_Acclimation vs Hot_control.Transplant | 1 | 0.3443779759 | 8.48534465 | 0.2783417653 | 0.004 | 0.060 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.0013738865 | 0.07238159 | 0.0045034760 | 0.632 | 1.000 | |
| Treatment.1_Acclimation vs Control.Transplant | 1 | -0.0041789330 | -0.31199952 | -0.0126376990 | 0.768 | 1.000 | |
| Treatment.1_Acclimation vs Treatment.Transplant | 1 | 0.0789743467 | 4.41748338 | 0.1809147697 | 0.066 | 0.990 | |
| Treatment.1_Acclimation vs Hot_control.Transplant | 1 | 0.0842190618 | 5.17868542 | 0.1905421598 | 0.062 | 0.930 | |
| Hot_control.1_Acclimation vs Control.Transplant | 1 | -0.0042784688 | -0.35701715 | -0.0144875787 | 0.789 | 1.000 | |
| Hot_control.1_Acclimation vs Treatment.Transplant | 1 | 0.0825042562 | 5.11970349 | 0.2038122581 | 0.072 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.Transplant | 1 | 0.0889170506 | 6.06518379 | 0.2161106028 | 0.051 | 0.765 | |
| Control.Transplant vs Treatment.Transplant | 1 | 0.1900996563 | 15.59722495 | 0.3497353247 | 0.007 | 0.105 | |
| Control.Transplant vs Hot_control.Transplant | 1 | 0.2049565481 | 17.96453178 | 0.3668886666 | 0.003 | 0.045 | . |
| Treatment.Transplant vs Hot_control.Transplant | 1 | -0.0002132009 | -0.01472772 | -0.0005667719 | 0.668 | 1.000 |
beta_richness_nmds_transplant7 <- beta_div_richness_transplant7$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant7_nmds, by = join_by(sample == newID))
beta_neutral_nmds_transplant7 <- beta_div_neutral_transplant7$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant7_nmds, by = join_by(sample == newID))
beta_phylo_nmds_transplant7 <- beta_div_phylo_transplant7$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant7_nmds, by = join_by(sample == newID))
beta_func_nmds_transplant7 <- beta_div_func_transplant7$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant7_nmds, by = join_by(sample == newID))p0<-beta_richness_nmds_transplant7 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_shape_manual(name="time_point",
breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
labels=c("Acclimation", "Transplant", "Transplant"),
values=c("circle","square","square")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_transplant7 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_shape_manual(name="time_point",
breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
labels=c("Acclimation", "Transplant", "Transplant"),
values=c("circle","square","square")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylo_nmds_transplant7 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_shape_manual(name="time_point",
breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
labels=c("Acclimation", "Transplant", "Transplant"),
values=c("circle","square","square")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")
p3<-beta_func_nmds_transplant7 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_shape_manual(name="time_point",
breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
labels=c("Acclimation", "Transplant", "Transplant"),
values=c("circle","square","square")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
theme_classic()+
theme(legend.position="none")6.3.5.6 Comparison between Acclimation vs Post-FMT1
post3 <- meta %>%
filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
post3.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post3))]
identical(sort(colnames(post3.counts)),sort(as.character(rownames(post3))))
post3_nmds <- sample_metadata %>%
filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1")6.3.5.7 Number of samples used
[1] 53
beta_div_richness_post3<-hillpair(data=post3.counts, q=0)
beta_div_neutral_post3<-hillpair(data=post3.counts, q=1)
beta_div_phylo_post3<-hillpair(data=post3.counts, q=1, tree=genome_tree)
beta_div_func_post3<-hillpair(data=post3.counts, q=1, dist=dist)#Arrange of metadata dataframe
post3_arrange<-post3[labels(beta_div_neutral_post3$S),]
post3_arrange$type_time <- interaction(post3_arrange$type, post3_arrange$time_point)6.3.5.7.1 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.099607 0.049803 9.5441 999 0.001 ***
Residuals 50 0.260911 0.005218
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.00100000 0.905
Hot_control 0.00102653 0.001
Treatment 0.88832670 0.00010131
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 1.2059071 | 0.0649048 | 3.913237 | 0.001 |
| Population | 1 | 1.7615474 | 0.0948107 | 5.716321 | 0.001 |
| time_point:Population | 1 | 0.5122847 | 0.0275724 | 1.662393 | 0.005 |
| Residual | 49 | 15.0998916 | 0.8127121 | NA | NA |
| Total | 52 | 18.5796308 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.3620815 | 1.052109 | 0.06169963 | 0.322 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.2800877 | 4.605444 | 0.22350616 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.6845657 | 1.998114 | 0.11101796 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.8437461 | 2.499232 | 0.14281954 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.1208022 | 3.568670 | 0.18236649 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.3606630 | 5.087152 | 0.24124415 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.7216200 | 2.172734 | 0.11956009 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.9551308 | 2.926054 | 0.16322910 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.2263345 | 4.039487 | 0.20157637 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 1.4319792 | 5.384836 | 0.25180628 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.8172413 | 3.194690 | 0.17558364 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.5796135 | 2.441615 | 0.13239702 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.5615418 | 1.729004 | 0.10335366 | 0.015 | 0.225 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.8438429 | 2.793772 | 0.14865413 | 0.002 | 0.030 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.3734921 | 1.268929 | 0.07799710 | 0.107 | 1.000 |
6.3.5.7.2 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00945 0.0094472 1.1428 999 0.291
Residuals 51 0.42161 0.0082669
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 5_Post-FMT1
1_Acclimation 0.284
5_Post-FMT1 0.2901
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 1.7277808 | 0.0986354 | 6.486824 | 0.001 |
| Population | 1 | 2.0558578 | 0.1173647 | 7.718565 | 0.001 |
| time_point:Population | 1 | 0.6819354 | 0.0389303 | 2.560276 | 0.005 |
| Residual | 49 | 13.0512643 | 0.7450696 | NA | NA |
| Total | 52 | 17.5168383 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.2316020 | 0.7712905 | 0.04598874 | 0.715 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.4015347 | 5.7562378 | 0.26457873 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.8332162 | 2.9081103 | 0.15380227 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 1.1719595 | 4.0685514 | 0.21336447 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.4260875 | 5.2413171 | 0.24675104 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.6347704 | 6.8326887 | 0.29925029 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.9517634 | 3.3715700 | 0.17404733 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 1.3127773 | 4.6298256 | 0.23585668 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.6713369 | 6.2395460 | 0.28056085 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 1.5409781 | 6.8338056 | 0.29928456 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.9133614 | 4.0964534 | 0.21451383 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.6954835 | 3.2951234 | 0.17077493 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.6051778 | 2.2508491 | 0.13047758 | 0.019 | 0.285 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 1.0528902 | 4.1436369 | 0.20570451 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.4150076 | 1.6372683 | 0.09840968 | 0.055 | 0.825 |
6.3.5.7.3 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.05132 0.051320 2.6745 999 0.103
Residuals 51 0.97861 0.019189
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 5_Post-FMT1
1_Acclimation 0.101
5_Post-FMT1 0.10812
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 0.4329638 | 0.1243360 | 7.717455 | 0.001 |
| Population | 1 | 0.2375991 | 0.0682323 | 4.235135 | 0.002 |
| time_point:Population | 1 | 0.0626513 | 0.0179918 | 1.116741 | 0.286 |
| Residual | 49 | 2.7489923 | 0.7894398 | NA | NA |
| Total | 52 | 3.4822065 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.04186923 | 0.4391642 | 0.02671451 | 0.729 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.15609416 | 2.5546889 | 0.13768428 | 0.034 | 0.510 | |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.19193367 | 2.9749922 | 0.15678490 | 0.024 | 0.360 | |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.14627288 | 1.7907381 | 0.10665035 | 0.154 | 1.000 | |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.25061348 | 3.6146185 | 0.18428187 | 0.011 | 0.165 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.0521838 | 0.20208192 | 0.003 | 0.045 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.26358465 | 4.3608960 | 0.21417997 | 0.010 | 0.150 | |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.25319427 | 3.2738422 | 0.17915456 | 0.032 | 0.480 | |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.39050120 | 5.9837393 | 0.27218933 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.14203376 | 5.4200212 | 0.25303529 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.09666753 | 2.3682173 | 0.13635351 | 0.017 | 0.255 | |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.09252600 | 2.9824958 | 0.15711821 | 0.010 | 0.150 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.01842535 | 0.4144162 | 0.02688498 | 0.786 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.05987967 | 1.7387847 | 0.09802164 | 0.123 | 1.000 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.03212966 | 0.6477782 | 0.04139746 | 0.708 | 1.000 |
6.3.5.7.4 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00541 0.0054137 0.2021 999 0.658
Residuals 51 1.36615 0.0267873
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 5_Post-FMT1
1_Acclimation 0.656
5_Post-FMT1 0.65494
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 0.0571984 | 0.0294799 | 1.5128525 | 0.181 |
| Population | 1 | 0.0239890 | 0.0123639 | 0.6344904 | 0.878 |
| time_point:Population | 1 | 0.0064542 | 0.0033265 | 0.1707088 | 0.642 |
| Residual | 49 | 1.8526072 | 0.9548297 | NA | NA |
| Total | 52 | 1.9402488 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.090450145 | 1.65575459 | 0.093779882 | 0.214 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.085769225 | 1.63605364 | 0.092767559 | 0.221 | 1.000 | |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.030318564 | 0.53607587 | 0.032418566 | 0.517 | 1.000 | |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.236457683 | 4.06299320 | 0.213135113 | 0.041 | 0.615 | |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.135603602 | 2.22854385 | 0.122255726 | 0.157 | 1.000 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.001373886 | 0.07238159 | 0.004503476 | 0.668 | 1.000 | |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.002173211 | 0.09402475 | 0.005842215 | 0.640 | 1.000 | |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.059119657 | 2.62461793 | 0.148917721 | 0.163 | 1.000 | |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.010911935 | 0.39816986 | 0.024281360 | 0.499 | 1.000 | |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | -0.002303582 | -0.11016709 | -0.006933181 | 0.740 | 1.000 | |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.058908140 | 2.91987617 | 0.162940644 | 0.145 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.005904278 | 0.23427876 | 0.014431116 | 0.527 | 1.000 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.116146557 | 4.72479132 | 0.239535681 | 0.066 | 0.990 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.050009298 | 1.70482607 | 0.096291602 | 0.238 | 1.000 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.012358590 | 0.42381202 | 0.027477774 | 0.491 | 1.000 |
beta_richness_nmds_post3 <- beta_div_richness_post3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post3_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_post3 <- beta_div_neutral_post3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post3_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_post3 <- beta_div_phylo_post3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post3_nmds, by = join_by(sample == Tube_code))
beta_func_nmds_post3 <- beta_div_func_post3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post3_nmds, by = join_by(sample == Tube_code))6.3.5.8 Comparison between Acclimation vs Post-FMT2
post4 <- meta %>%
filter(time_point == "1_Acclimation" | time_point == "6_Post-FMT2")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
post4.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post4))]
identical(sort(colnames(post4.counts)),sort(as.character(rownames(post4))))
post4_nmds <- sample_metadata %>%
filter(time_point == "1_Acclimation" | time_point == "6_Post-FMT2")6.3.5.9 Number of samples used
[1] 54
beta_div_richness_post4<-hillpair(data=post4.counts, q=0)
beta_div_neutral_post4<-hillpair(data=post4.counts, q=1)
beta_div_phylo_post4<-hillpair(data=post4.counts, q=1, tree=genome_tree)
beta_div_func_post4<-hillpair(data=post4.counts, q=1, dist=dist)#Arrange of metadata dataframe
post4_arrange<-post4[labels(beta_div_neutral_post4$S),]
post4_arrange$type_time <- interaction(post4_arrange$type, post4_arrange$time_point)6.3.5.9.1 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.06809 0.034047 3.8471 999 0.034 *
Residuals 51 0.45135 0.008850
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.0290000 0.887
Hot_control 0.0349385 0.008
Treatment 0.8855174 0.0047257
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 0.8124061 | 0.0462232 | 2.847438 | 0.001 |
| Population | 1 | 2.0491994 | 0.1165926 | 7.182331 | 0.001 |
| time_point:Population | 1 | 0.4485668 | 0.0255219 | 1.572202 | 0.002 |
| Residual | 50 | 14.2655595 | 0.8116623 | NA | NA |
| Total | 53 | 17.5757317 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.3620815 | 1.052109 | 0.06169963 | 0.331 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.2800877 | 4.605444 | 0.22350616 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.8430295 | 2.845779 | 0.15100353 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.5232174 | 1.683240 | 0.09518843 | 0.027 | 0.405 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.1217138 | 3.634271 | 0.18509835 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.3606630 | 5.087152 | 0.24124415 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.9130048 | 3.195028 | 0.16645080 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.5959230 | 1.984036 | 0.11032208 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.2747787 | 4.275366 | 0.21086503 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.6397330 | 2.913695 | 0.15405213 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.4575447 | 6.224524 | 0.28007456 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.3276169 | 1.412318 | 0.08111028 | 0.046 | 0.690 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.6463814 | 2.560441 | 0.13795154 | 0.001 | 0.015 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.4796256 | 1.916520 | 0.10696943 | 0.001 | 0.015 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.1305044 | 4.268317 | 0.21059061 | 0.001 | 0.015 | . |
6.3.5.9.2 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01544 0.0154447 2.0972 999 0.167
Residuals 52 0.38294 0.0073643
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 6_Post-FMT2
1_Acclimation 0.162
6_Post-FMT2 0.15357
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 1.0151664 | 0.0602602 | 3.909554 | 0.001 |
| Population | 1 | 2.2827471 | 0.1355037 | 8.791191 | 0.001 |
| time_point:Population | 1 | 0.5653146 | 0.0335570 | 2.177109 | 0.001 |
| Residual | 50 | 12.9831505 | 0.7706790 | NA | NA |
| Total | 53 | 16.8463787 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.2316020 | 0.7712905 | 0.04598874 | 0.747 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.4015347 | 5.7562378 | 0.26457873 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.1746426 | 4.5564741 | 0.22165640 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.5286441 | 1.9819408 | 0.11021840 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.3443224 | 4.9104417 | 0.23483204 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.6347704 | 6.8326887 | 0.29925029 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.3540292 | 5.3398081 | 0.25022756 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.6311089 | 2.4041625 | 0.13063146 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.6125755 | 5.9825981 | 0.27215155 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.6202327 | 3.1519868 | 0.16457754 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.5701179 | 7.6327037 | 0.32297209 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.3634438 | 1.7083388 | 0.09647087 | 0.034 | 0.510 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 1.0227481 | 4.6483346 | 0.22511910 | 0.001 | 0.015 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.5010202 | 2.2065321 | 0.12119453 | 0.001 | 0.015 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.3619424 | 5.7710313 | 0.26507845 | 0.001 | 0.015 | . |
6.3.5.9.3 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.06978 0.069777 5.0345 999 0.026 *
Residuals 52 0.72071 0.013860
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 6_Post-FMT2
1_Acclimation 0.018
6_Post-FMT2 0.029131
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 0.4131659 | 0.1296152 | 8.500864 | 0.002 |
| Population | 1 | 0.2372445 | 0.0744265 | 4.881291 | 0.001 |
| time_point:Population | 1 | 0.1070826 | 0.0335931 | 2.203218 | 0.030 |
| Residual | 50 | 2.4301410 | 0.7623651 | NA | NA |
| Total | 53 | 3.1876340 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.04186923 | 0.4391642 | 0.02671451 | 0.744 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.15609416 | 2.5546889 | 0.13768428 | 0.033 | 0.495 | |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.26322331 | 4.3060281 | 0.21205664 | 0.004 | 0.060 | |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.16047895 | 2.5405742 | 0.13702781 | 0.034 | 0.510 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.25529510 | 4.0109138 | 0.20043631 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.0521838 | 0.20208192 | 0.003 | 0.045 | . |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.36496892 | 6.3966666 | 0.28560797 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.22628210 | 3.8292220 | 0.19311005 | 0.016 | 0.240 | |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.34830814 | 5.8463335 | 0.26761166 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.10002871 | 4.3836237 | 0.21505615 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.12577510 | 5.0601287 | 0.24027055 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.06334378 | 2.4997737 | 0.13512455 | 0.015 | 0.225 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.05927454 | 2.3820253 | 0.12958449 | 0.027 | 0.405 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.06906280 | 2.7224602 | 0.14541146 | 0.003 | 0.045 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.11081709 | 4.0436561 | 0.20174244 | 0.001 | 0.015 | . |
6.3.5.9.4 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00538 0.0053768 0.1915 999 0.686
Residuals 52 1.46001 0.0280772
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 6_Post-FMT2
1_Acclimation 0.681
6_Post-FMT2 0.66348
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| time_point | 1 | 0.0451272 | 0.0242533 | 1.2655291 | 0.221 |
| Population | 1 | 0.0004181 | 0.0002247 | 0.0117261 | 0.292 |
| time_point:Population | 1 | 0.0321822 | 0.0172961 | 0.9025046 | 0.314 |
| Residual | 50 | 1.7829366 | 0.9582260 | NA | NA |
| Total | 53 | 1.8606640 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 9.045014e-02 | 1.65575459 | 0.0937798825 | 0.240 | 1 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 8.576922e-02 | 1.63605364 | 0.0927675587 | 0.234 | 1 | |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.171666e-01 | 2.16526354 | 0.1191980254 | 0.194 | 1 | |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.134013e-01 | 2.16495643 | 0.1191831338 | 0.183 | 1 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 6.533744e-02 | 0.94814395 | 0.0559438221 | 0.263 | 1 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.373886e-03 | 0.07238159 | 0.0045034760 | 0.629 | 1 | |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | -4.929805e-04 | -0.02385162 | -0.0014929516 | 0.697 | 1 | |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 2.443617e-03 | 0.12903840 | 0.0080003779 | 0.567 | 1 | |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 6.968380e-03 | 0.19647180 | 0.0121305310 | 0.571 | 1 | |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 3.866607e-04 | 0.02093980 | 0.0013070267 | 0.682 | 1 | |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | -5.633463e-05 | -0.00336651 | -0.0002104511 | 0.702 | 1 | |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 2.011448e-03 | 0.06046867 | 0.0037650628 | 0.767 | 1 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | -8.527330e-03 | -0.46290555 | -0.0297935723 | 0.847 | 1 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | -1.648721e-03 | -0.04717131 | -0.0029569243 | 0.894 | 1 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 4.367477e-03 | 0.13147026 | 0.0081499244 | 0.695 | 1 |
beta_richness_nmds_post4 <- beta_div_richness_post4$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post4_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_post4 <- beta_div_neutral_post4$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post4_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_post4 <- beta_div_phylo_post4$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post4_nmds, by = join_by(sample == Tube_code))
beta_func_nmds_post4 <- beta_div_func_post4$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post4_nmds, by = join_by(sample == Tube_code))6.3.6 6. Are there differences between the control and the treatment group?
6.3.6.2 Number of samples used
[1] 26
beta_div_richness_post1<-hillpair(data=post1.counts, q=0)
beta_div_neutral_post1<-hillpair(data=post1.counts, q=1)
beta_div_phylo_post1<-hillpair(data=post1.counts, q=1, tree=genome_tree)
beta_div_func_post1<-hillpair(data=post1.counts, q=1, dist=dist)6.3.6.2.1 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.017675 0.0088373 2.3825 999 0.1
Residuals 23 0.085312 0.0037092
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.0080000 0.674
Hot_control 0.0068795 0.200
Treatment 0.6248469 0.2084296
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| species | 1 | 0.6340254 | 0.0768024 | 2.065607 | 0.004 |
| type | 1 | 0.5615418 | 0.0680222 | 1.829461 | 0.005 |
| Residual | 23 | 7.0597099 | 0.8551754 | NA | NA |
| Total | 25 | 8.2552771 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Control vs Treatment 1 0.5615418 1.729004 0.1033537 0.017 0.051
2 Control vs Hot_control 1 0.8438429 2.793772 0.1486541 0.002 0.006 *
3 Treatment vs Hot_control 1 0.3734921 1.268929 0.0779971 0.107 0.321
6.3.6.2.2 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.011001 0.0055005 0.6303 999 0.579
Residuals 23 0.200714 0.0087267
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.21400 0.960
Hot_control 0.21166 0.466
Treatment 0.95468 0.43604
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| species | 1 | 0.7907904 | 0.1076445 | 3.056657 | 0.001 |
| type | 1 | 0.6051778 | 0.0823784 | 2.339205 | 0.012 |
| Residual | 23 | 5.9503501 | 0.8099772 | NA | NA |
| Total | 25 | 7.3463184 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Control vs Treatment 1 0.6051778 2.250849 0.13047758 0.009 0.027 .
2 Control vs Hot_control 1 1.0528902 4.143637 0.20570451 0.002 0.006 *
3 Treatment vs Hot_control 1 0.4150076 1.637268 0.09840968 0.049 0.147
6.3.6.2.3 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.00440 0.0021994 0.1369 999 0.906
Residuals 23 0.36941 0.0160614
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.92300 0.677
Hot_control 0.91505 0.783
Treatment 0.63312 0.73046
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| species | 1 | 0.0560850 | 0.0531376 | 1.3149967 | 0.243 |
| type | 1 | 0.0184254 | 0.0174571 | 0.4320099 | 0.797 |
| Residual | 23 | 0.9809570 | 0.9294053 | NA | NA |
| Total | 25 | 1.0554673 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Control vs Treatment 1 0.01842535 0.4144162 0.02688498 0.771 1.000
2 Control vs Hot_control 1 0.05987967 1.7387847 0.09802164 0.108 0.324
3 Treatment vs Hot_control 1 0.03212966 0.6477782 0.04139746 0.710 1.000
6.3.6.2.4 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.00400 0.0020014 0.145 999 0.852
Residuals 23 0.31753 0.0138057
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.56800 0.755
Hot_control 0.59817 0.848
Treatment 0.75141 0.83718
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| species | 1 | 0.0024979 | 0.0033024 | 0.0900845 | 0.655 |
| type | 1 | 0.1161466 | 0.1535542 | 4.1887855 | 0.073 |
| Residual | 23 | 0.6377435 | 0.8431434 | NA | NA |
| Total | 25 | 0.7563879 | 1.0000000 | NA | NA |
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Control vs Treatment 1 0.11614656 4.724791 0.23953568 0.072 0.216
2 Control vs Hot_control 1 0.05000930 1.704826 0.09629160 0.247 0.741
3 Treatment vs Hot_control 1 0.01235859 0.423812 0.02747777 0.480 1.000
beta_richness_nmds_post1 <- beta_div_richness_post1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post1_nmds, by = join_by(sample == Tube_code))
beta_neutral_nmds_post1 <- beta_div_neutral_post1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post1_nmds, by = join_by(sample == Tube_code))
beta_phylogenetic_nmds_post1 <- beta_div_phylo_post1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post1_nmds, by = join_by(sample == Tube_code))
beta_functional_nmds_post1 <- beta_div_func_post1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post1_nmds, by = join_by(sample == Tube_code))p0<-beta_richness_nmds_post1 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_post1 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylogenetic_nmds_post1 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")
p3<-beta_functional_nmds_post1 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
theme_classic()+
theme(legend.position="none")6.3.6.4 Number of samples used
[1] 27
beta_div_richness_post2<-hillpair(data=post2.counts, q=0)
beta_div_neutral_post2<-hillpair(data=post2.counts, q=1)
beta_div_phylo_post2<-hillpair(data=post2.counts, q=1, tree=genome_tree)
beta_div_func_post2<-hillpair(data=post2.counts, q=1, dist=dist)6.3.6.4.1 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.002011 0.0010056 0.1982 999 0.829
Residuals 24 0.121775 0.0050740
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.70100 0.830
Hot_control 0.67789 0.634
Treatment 0.79246 0.59820
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| type | 2 | 1.504341 | 0.1967776 | 2.939822 | 0.001 |
| Residual | 24 | 6.140538 | 0.8032224 | NA | NA |
| Total | 26 | 7.644879 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Treatment vs Control | 1 | 0.6463814 | 2.560441 | 0.1379515 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.4796256 | 1.916520 | 0.1069694 | 0.001 | 0.003 | * |
| Control vs Hot_control | 1 | 1.1305044 | 4.268317 | 0.2105906 | 0.001 | 0.003 | * |
6.3.6.4.2 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.008262 0.0041311 0.8024 999 0.447
Residuals 24 0.123559 0.0051483
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.45200 0.661
Hot_control 0.44675 0.245
Treatment 0.65989 0.25095
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| type | 2 | 1.923807 | 0.2603795 | 4.224537 | 0.001 |
| Residual | 24 | 5.464666 | 0.7396205 | NA | NA |
| Total | 26 | 7.388473 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Treatment vs Control | 1 | 1.0227481 | 4.648335 | 0.2251191 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.5010202 | 2.206532 | 0.1211945 | 0.002 | 0.006 | * |
| Control vs Hot_control | 1 | 1.3619424 | 5.771031 | 0.2650785 | 0.001 | 0.003 | * |
6.3.6.4.3 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.000407 0.0002034 0.0487 999 0.964
Residuals 24 0.100305 0.0041794
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.93600 0.856
Hot_control 0.93765 0.795
Treatment 0.83933 0.76015
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| type | 2 | 0.1594363 | 0.2042241 | 3.079623 | 0.001 |
| Residual | 24 | 0.6212564 | 0.7957759 | NA | NA |
| Total | 26 | 0.7806927 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Treatment vs Control | 1 | 0.05927454 | 2.382025 | 0.1295845 | 0.028 | 0.084 | |
| Treatment vs Hot_control | 1 | 0.06906280 | 2.722460 | 0.1454115 | 0.004 | 0.012 | . |
| Control vs Hot_control | 1 | 0.11081709 | 4.043656 | 0.2017424 | 0.002 | 0.006 | * |
6.3.6.4.4 Functional
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.01126 0.0056302 0.2861 999 0.811
Residuals 24 0.47233 0.0196806
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.55900 0.649
Hot_control 0.48255 0.828
Treatment 0.60116 0.75643
| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| type | 2 | -0.0038724 | -0.0056213 | -0.0670788 | 0.923 |
| Residual | 24 | 0.6927468 | 1.0056213 | NA | NA |
| Total | 26 | 0.6888744 | 1.0000000 | NA | NA |
| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Treatment vs Control | 1 | -0.008527330 | -0.46290555 | -0.029793572 | 0.855 | 1 | |
| Treatment vs Hot_control | 1 | -0.001648721 | -0.04717131 | -0.002956924 | 0.907 | 1 | |
| Control vs Hot_control | 1 | 0.004367477 | 0.13147026 | 0.008149924 | 0.695 | 1 |
beta_richness_nmds_post2 <- beta_div_richness_post2$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post2_nmds, by = join_by(sample == Tube_code))
beta_neutral_nmds_post2 <- beta_div_neutral_post2$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post2_nmds, by = join_by(sample == Tube_code))
beta_phylogenetic_nmds_post2 <- beta_div_phylo_post2$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post2_nmds, by = join_by(sample == Tube_code))
beta_functional_nmds_post2 <- beta_div_func_post2$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post2_nmds, by = join_by(sample == Tube_code))p0<-beta_richness_nmds_post2 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_post2 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylogenetic_nmds_post2 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")
p3<-beta_functional_nmds_post2 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
theme_classic()+
theme(legend.position="none")